---
title: "Un joli titre pour le dashboard"
author: "Sofia Chemolli & Amélie Rivet"
output:
flexdashboard::flex_dashboard:
orientation: columns
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(ggplot2)
library(tidyverse)
library(dplyr)
library(plotly)
library(flexdashboard)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Visualisation 1
```{r data, eval = TRUE, echo = FALSE, results='hide', message = FALSE, warning=FALSE}
# Espérance de vie a la naissance
dta_off <- read.csv("Dta_off.csv", sep=",", stringsAsFactors = T)
dta_off <- dta_off[,3:23]
colnames(dta_off) <- c("Country","ISO3","2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018")
dta_off <- dta_off %>%
pivot_longer(c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018"))
colnames(dta_off)[3:4] <- c("Year", "Life.expectancy")
dta_off$Year <- as.factor(dta_off$Year)
dta_off <- dta_off[-c(4558:nrow(dta_off)),]
# GDP par habitant
dta_GDP <- read.csv("GDPDataBank.csv", sep=",", stringsAsFactors = T)
dta_GDP <- dta_GDP[,4:23]
colnames(dta_GDP) <- c("ISO3","2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018")
dta_GDP <- dta_GDP %>%
pivot_longer(c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018"))
colnames(dta_GDP)[2:3] <- c("Year", "GDP")
dta_GDP$Year <- as.factor(dta_GDP$Year)
# Dépenses sante % GDP
dta_dep <- read.csv("HealthExpDataBank.csv", sep=",", stringsAsFactors = T)
dta_dep <- dta_dep[,4:23]
colnames(dta_dep) <- c("ISO3","2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018")
dta_dep <- dta_dep %>%
pivot_longer(c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018"))
colnames(dta_dep)[2:3] <- c("Year", "Depense")
dta_dep$Year <- as.factor(dta_dep$Year)
# Anemie chez les enfants
dta_anemia <- read.csv("WHO_anemia.csv", sep=';', dec=',', stringsAsFactors = T)
dta_anemia$Year <- as.factor(dta_anemia$Year)
# Acces a l'eau potable
dta_water <- read.csv("WaterDataBank.csv", sep=",", stringsAsFactors = T)
dta_water <- dta_water[,4:23]
colnames(dta_water) <- c("ISO3","2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015", "2016","2017","2018")
dta_water <- dta_water %>%
pivot_longer(c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015", "2016","2017","2018"))
colnames(dta_water)[2:3] <- c("Year", "Water")
dta_water$Year <- as.factor(dta_water$Year)
dta <- left_join(dta_off, dta_dep, by = c("ISO3","Year"))
dta <- left_join(dta, dta_GDP, by = c("ISO3","Year"))
dta <- left_join(dta, dta_anemia[,-2], by = c("ISO3","Year"))
dta <- left_join(dta, dta_water, by = c("ISO3","Year"))
# Amélie
dta.diff <- dta %>%
group_by(ISO3) %>%
summarise(Life.expectancy.diff = Life.expectancy[Year=='2018'] - Life.expectancy[Year=='2000'],
Depense.diff = Depense[Year=='2018'] - Depense[Year=='2000'],
GDP.diff = GDP[Year=='2018'] - GDP[Year=='2000'],
Anemie.diff = Anemie[Year=='2018'] - Anemie[Year=='2000'],
Water.diff = Water[Year=='2018'] - Water[Year=='2000']) %>%
arrange(Life.expectancy.diff)
# Sofia
diff.ISO3 <- dta_off %>%
filter(Year==2000 | Year==2018) %>%
group_by(ISO3) %>%
summarise(Life.expectancy.diff = Life.expectancy[Year=='2018'] - Life.expectancy[Year=='2000'],
Country = Country,
Life.expectancy.2000 = Life.expectancy[Year=='2000'],
Life.expectancy.2018 = Life.expectancy[Year=='2018']) %>%
arrange(Life.expectancy.diff)
```
```{r vis1, echo = FALSE, message = FALSE, warning=FALSE}
fig <- plot_ly(diff.ISO3,
type = 'choropleth',
locations = diff.ISO3$ISO3,
z = diff.ISO3$Life.expectancy.diff,
text = paste("Pays : ", diff.ISO3$Country, '
',
"Esperance de vie en 2000 : ", round(diff.ISO3$Life.expectancy.2000,1), '
',
"Esperance de vie en 2018 : ", round(diff.ISO3$Life.expectancy.2018,1), '
',
"Difference : ", round(diff.ISO3$Life.expectancy.diff,1)),
colorscale = "YlGnBu",
hoverinfo = 'text')
m <- list(l = 50, r = 50, b = 100, t = 100, pad = 4)
fig %>%
colorbar(title = list(
text = paste0("Différence de \nl'espérance de vie \nà la naissance \nentre 2018 et 2000 \n(en années)"),
font = list(size=12)),
x = 1, y = 0.7, len = 0.4) %>%
layout(title = list(text = paste0("Différence de l'espérance de vie moyenne par pays" , '
', '', "entre 2018 et 2000"),
font=list(size=20), xanchor = "middle", y=0.8, autosize = F,
width = 500, height = 500, margin = m))
```
Column {.tabset data-width=500}
-----------------------------------------------------------------------
### Visualisation 2
```{r, eval = TRUE, echo = FALSE, results='hide', message = FALSE, warning=FALSE, fig.keep='none'}
expectancy <- inner_join(dta, dta.diff, by='ISO3')
expectancy.2000 <- expectancy %>%
filter(Year==2000)
expectancy.2000[,4:13]<-round(expectancy.2000[,4:13],1)
summary(expectancy.2000)
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
g1 <- ggplotly(ggplot(data = expectancy.2000,
aes(x = GDP.diff, y = Life.expectancy.diff,
text=paste("Pays : ",Country,
"
Espérance de vie en 2000 : ", Life.expectancy,
"
GDP en 2000 : ", GDP)),
na.rm=TRUE) +
geom_point(aes(color=Life.expectancy, size=GDP)) +
scale_color_gradient(low ="#FFFF99",high = "#009933") +
scale_y_continuous(breaks=seq(-10, 20, 5)) +
scale_x_continuous(breaks=seq(-1000, 101000, 25000)) +
labs(x = "Différence de GDP par habitant \nentre 2000 et 2018",
y = "Différence d'espérance de vie \nentre 2000 et 2018",
colour = "Espérance de vie \n en 2000",
size = "GDP par habitant \n en 2000") +
ggtitle("Représentation de l'espérance de vie en fonction du GDP par habitant") +
theme(plot.title = element_text(face="bold", hjust=0.5, size=10),
axis.title.x = element_text(size=9),
axis.title.y = element_text(size=9),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "#CCCCCC"),
plot.margin = unit(c(1,1,1,1), "cm")), tooltip="text")
# g1 %>% layout(showlegend=TRUE)
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
g2 <- ggplotly(ggplot(data = expectancy.2000,
aes(x = Depense.diff, y = Life.expectancy.diff,
text = paste("Pays : ", Country,
"
Espérance de vie en 2000 : ", Life.expectancy,
"
Dépenses en santé en % GDP en 2000 : ", Depense," %")),
na.rm=TRUE) +
geom_point(aes(color=Life.expectancy, size=Depense)) +
scale_color_gradient(low ="#FFFF99",high = "#009933") +
scale_y_continuous(breaks=seq(-10, 20, 5)) +
scale_x_continuous(limits=c(-5,7.5), breaks=seq(-5, 7.5, 2.5)) +
labs(x = "Différence du pourcentage de dépenses \nen santé en pourcentage du GDP \nentre 2000 et 2018",
y = "Différence d'espérance de vie \nentre 2000 et 2018",
colour = "Espérance de vie \n en 2000",
size = "Dépenses en santé \n par habitant en 2000") +
ggtitle("Représentation de la relation espérance de vie - dépense en santé") +
theme(plot.title = element_text(face="bold", hjust=0.5, size=10),
axis.title.x = element_text(size=9),
axis.title.y = element_text(size=9),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "#CCCCCC"),
plot.margin = unit(c(1,1,1,1), "cm")), tooltip="text")
# g2 %>% layout(showlegend=TRUE)
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
g3 <- ggplotly(ggplot(data = expectancy.2000,
aes(x = Anemie.diff, y = Life.expectancy.diff,
text=paste("Pays : ",Country,
"
Espérance de vie en 2000 : ", Life.expectancy,
"
Enfants de moins de 5 ans \n atteint d'anémie en 2000 : ", Anemie, "%")),
na.rm=TRUE) +
geom_point(aes(color=Life.expectancy, size=Anemie)) +
scale_color_gradient(low ="#FFFF99",high = "#009933") +
scale_y_continuous(breaks=seq(-10, 20, 5)) +
scale_x_continuous(limits=c(-30, 5), breaks=seq(-30,5,5))+
labs(x = "Différence de pourcentage d'enfants de moins de \n5 ans atteints d'anémie \nentre 2000 et 2018",
y = "Différence d'espérance de vie \nentre 2000 et 2018",
colour = "Espérance de vie \n en 2000",
size = "% d'enfant atteints \n d'anémie en 2000") +
ggtitle("Représentation de la relation espérance de vie - Anémie") +
theme(plot.title = element_text(face="bold", hjust=0.5, size=10),
axis.title.x = element_text(size=9),
axis.title.y = element_text(size=9),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "#CCCCCC"),
plot.margin = unit(c(1,1,1,1), "cm")), tooltip="text")
# g3 %>% layout(showlegend=TRUE)
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
g4 <- ggplotly(ggplot(data = expectancy.2000,
aes(x = Water.diff, y = Life.expectancy.diff,
text=paste("Pays : ",Country,
"
Espérance de vie en 2000 : ", Life.expectancy,
"
Accès à l'eau potable en 2000 : ", Water, "%")),
na.rm=TRUE) +
geom_point(aes(color=Life.expectancy, size=Water)) +
scale_color_gradient(low ="#FFFF99",high = "#009933") +
scale_y_continuous(breaks=seq(-10, 20, 5)) +
scale_x_continuous(limits=c(-10,35), breaks=seq(-10, 30, 10)) +
labs(x = "Différence du pourcentage de personnes \nayant accès à l'eau potable \nentre 2000 et 2018",
y = "Différence d'espérance de vie \nentre 2000 et 2018",
colour = "Espérance de vie \n en 2000",
size = "% de personnes \n ayant accès à l'eau potable en 2000") +
ggtitle("Représentation de la rélation entre les différences d'espérance de vie \n et de plusieurs indicateurs entre 2000 et 2018") +
theme(plot.title = element_text(face="bold", hjust=0.5, size=10),
axis.title.x = element_text(size=9),
axis.title.y = element_text(size=9),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "#CCCCCC"),
plot.margin = unit(c(1,1,1,1), "cm")), tooltip="text")
# g4 %>% layout(showlegend=TRUE)
```
```{r}
subplot(g1, g2, g3, g4, nrows = 2, margin = 0.1, titleX = T, titleY = T)
```
### Visualisation 3
```{r, echo = FALSE, message = FALSE, warning=FALSE}
dta_SYR <- subset(dta_off, ISO3 == "SYR")
p1 <- ggplot(data = dta_SYR) +
geom_line(aes(x = Year, y = Life.expectancy),
color = "#00AFBB", size = 1, group=1) +
geom_point(aes(x = Year, y = Life.expectancy, group=1)) +
scale_y_continuous(limits=c(68,75), breaks=seq(68, 75, 1)) +
annotate(geom = "segment",
x = 12, xend = 19,
y = 68.8, yend = 68.8,
arrow = arrow(length = unit(2, "mm"))) +
annotate(geom = "text",
x = 13, y = 69,
label = "Guerre civile depuis 2011",
hjust = "left") +
theme(plot.margin = unit(c(1,1,1,1), "cm")) +
annotate(geom = "segment",
x = 0, xend = 19,
y = 68, yend = 68,
arrow = arrow(length = unit(2, "mm"))) +
annotate(geom = "text",
x = 9, y = 68.2,
label = "Chef du gouvernement : Bashar el-Assad") +
annotate(geom = "rect",
xmin = 12, xmax = 19,
ymin = 68, ymax = 75,
alpha = .3, fill = "skyblue3") +
annotate(geom = "curve",
x = 6.9, xend = 6,
y = 74.8, yend = 74.5,
curvature = .5,
arrow = arrow(length = unit(2, "mm"))) +
annotate(geom = "text",
x = 7, y = 74.8,
label = "Retrait des troupes syriennes du Liban",
hjust = "left") +
theme_classic() +
labs(title="Evolution de l'espérance de vie en Syrie",
subtitle='Entre 2000 et 2018',
x = 'Années',
y = 'Espérance de vie moyenne à la naissance \n (en années)')
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
p2 <- ggplot(data = dta_off[dta_off$ISO3 == "GRD",],
aes(x = Year, y = Life.expectancy, group=1)) +
geom_line(color = "#00AFBB", size = 2) +
geom_point() +
# Ouragan Ivan 2004
annotate(geom = "curve",
x = 3.5, xend = 5,
y = 73.15, yend = dta_off$Life.expectancy[dta_off$Country=="Grenada"&dta_off$Year=="2004"] + 0.03,
curvature = -0.2,
arrow = arrow(length = unit(2, "mm")),
col ="#00AFBB") +
annotate(geom = "text",
x = 3.4, y = 73.15,
label = "Ouragan Ivan",
hjust = "right",
col = "#00AFBB") +
geom_segment(aes(x=5, xend=5,
y = min(dta_off$Life.expectancy[dta_off$Country=="Grenada"]),
yend = dta_off$Life.expectancy[dta_off$Country=="Grenada"&dta_off$Year=="2004"]),
linetype="longdash", col ="#00AFBB") +
# Ouragan Emily 2005
annotate(geom = "curve",
x = 7.5, xend = 6,
y = 73.2, yend = dta_off$Life.expectancy[dta_off$Country=="Grenada"&dta_off$Year=="2005"] + 0.03,
curvature = .2,
arrow = arrow(length = unit(2, "mm"))) +
annotate(geom = "text",
x = 7.6, y = 73.2,
label = "Ouragan Emily",
hjust = "left") +
geom_segment(aes(x=6, xend=6,
y = min(dta_off$Life.expectancy[dta_off$Country=="Grenada"]),
yend = dta_off$Life.expectancy[dta_off$Country=="Grenada"&dta_off$Year=="2005"]),
linetype="longdash") +
theme_classic() +
labs(title="Evolution de l'espérance de vie de Grenada", # ajout du titre
subtitle='Entre 2000 et 2018') # ajout du sous titre
```
```{r, echo = FALSE, message = FALSE, warning=FALSE}
GDP_VEN <- read.csv("GDP_VEN.csv", sep=";")
colnames(GDP_VEN) <- c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018")
GDP_VEN <- GDP_VEN %>%
pivot_longer(c("2000", "2001", "2002","2003", "2004", "2005","2006", "2007", "2008","2009","2010", "2011", "2012","2013", "2014", "2015","2016","2017","2018"))
colnames(GDP_VEN)[1:2] <- c("Year", "GDP")
GDP_VEN$Year <- as.factor(GDP_VEN$Year)
GDP_VEN$GDP_obs <- c(GDP_VEN$GDP[1:15], rep(NA,4))
GDP_VEN$GDP_pred <- c(rep(NA,14),GDP_VEN$GDP[15:19])
dta_dual_y <- data.frame(dta_off[dta_off$ISO3 == "VEN",])
dta_dual_y$GDP_obs <- c(GDP_VEN$GDP[1:15], rep(NA,4))
dta_dual_y$GDP_pred <- c(rep(NA,14),GDP_VEN$GDP[15:19])
old.y <- list(
side = "left"
)
new.y <- list(
overlaying = "y",
side = "right",
title = "GDP par habitant (USD)"
)
p3 <- plot_ly(dta_dual_y) %>%
add_lines(x = ~Year, y = ~Life.expectancy, yaxis="y1", name="Esperance de vie",
line = list(color = 'black', width = 2)) %>%
add_lines(x = ~Year, y = ~GDP_obs, yaxis = "y2", name="GDP (données officielles)",
line = list(color = '#00AFBB', width = 2)) %>%
add_lines(x = ~Year, y = ~GDP_pred, yaxis = "y2", name="GDP (données estimées)",
line = list(color = '#00AFBB', width = 2, dash = 'dot')) %>%
layout(yaxis2 = new.y,
yaxis = list(old.y, showgrid = F, title="Espérance de vie moyenne à la naissance \n (en années)"),
xaxis = list(title="Années", showgrid = F),
legend = list(title=list(text=''), y = 73,
textfont = list(family = "Arial", size = 14, color = toRGB("black"))),
title = list(text = paste0("Evolution de l'espérance de vie du Venezuela", '
',
'', "Entre 2000 et 2018"),
xanchor = 'left', x=0.1),
shapes = list(
list(type = "rect",
fillcolor = "lightblue", line = list(color = "lightblue"), opacity = 0.2,
x0 = "0", x1 = "13",
y0 = 72, y1 = 73.5),
list(type = "rect",
fillcolor = "#009EFF", line = list(color = "#009EFF"), opacity = 0.2,
x0 = "13", x1 = "18",
y0 = 72, y1 = 73.5))) %>%
add_text(showlegend = FALSE,
x = c("2004","2016"), y = c(73.4, 73.4),
text = c("Présidence d'Hugo Chávez","Présidence de \nNicolás Maduro"),
textfont = list(family = "Arial", size = 14, color = toRGB("black")))
```
```{r}
par(mfrow=c(3,1))
p1
p2
p3
```
